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Merrouchi M, Benyoussef Y, Skittou M, Atifi K, Gadi T. ConvCoroNet: a deep convolutional neural network optimized with iterative thresholding algorithm for Covid-19 detection using chest X-ray images. J Biomol Struct Dyn 2024; 42:5699-5712. [PMID: 37354142 DOI: 10.1080/07391102.2023.2227726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Accepted: 06/15/2023] [Indexed: 06/26/2023]
Abstract
Covid-19 is a global pandemic. Early and accurate detection of positive cases prevent the further spread of this epidemic and help to treat rapidly the infected patients. During the peak of this epidemic, there was an insufficiency of Covid-19 test kits. In addition, this technique takes a considerable time in the diagnosis. Hence the need to find fast, accurate and low-cost method to replace or supplement RT PCR-based methods. Covid-19 is a respiratory disease, chest X-ray images are often used to diagnose pneumonia. From this perspective, these images can play an important role in the Covid-19 detection. In this article, we propose ConvCoroNet, a deep convolutional neural network model optimized with new method based on iterative thresholding algorithm to detect coronavirus automatically from chest X-ray images. ConvCoroNet is trained on a dataset prepared by collecting chest X-ray images of Covid-19, pneumonia and normal cases from publically datasets. The experimental results of our proposed model show a high accuracy of 99.50%, sensitivity of 98.80% and specificity of 99.85% when detecting Covid-19 from chest X-ray images. ConvCoroNet achieves promising results in the automatic detection of Covid-19 from chest X-ray images. It may be able to help radiologists in the Covid-19 detection by reducing the examination time of X-ray images.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- M Merrouchi
- Faculty of Science and Technology, Hassan First, Settat, Morocco
| | - Y Benyoussef
- National School of Applied Sciences, Hassan First, Berrechid, Morocco
| | - M Skittou
- Faculty of Science and Technology, Hassan First, Settat, Morocco
| | - K Atifi
- Faculty of Science and Technology, Hassan First, Settat, Morocco
| | - T Gadi
- Faculty of Science and Technology, Hassan First, Settat, Morocco
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2
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Wang S, Ren J, Guo X. A high-accuracy lightweight network model for X-ray image diagnosis: A case study of COVID detection. PLoS One 2024; 19:e0303049. [PMID: 38889106 PMCID: PMC11185471 DOI: 10.1371/journal.pone.0303049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 04/15/2024] [Indexed: 06/20/2024] Open
Abstract
The Coronavirus Disease 2019(COVID-19) has caused widespread and significant harm globally. In order to address the urgent demand for a rapid and reliable diagnostic approach to mitigate transmission, the application of deep learning stands as a viable solution. The impracticality of many existing models is attributed to excessively large parameters, significantly limiting their utility. Additionally, the classification accuracy of the model with few parameters falls short of desirable levels. Motivated by this observation, the present study employs the lightweight network MobileNetV3 as the underlying architecture. This paper incorporates the dense block to capture intricate spatial information in images, as well as the transition layer designed to reduce the size and channel number of the feature map. Furthermore, this paper employs label smoothing loss to address the inter-class similarity effects and uses class weighting to tackle the problem of data imbalance. Additionally, this study applies the pruning technique to eliminate unnecessary structures and further reduce the number of parameters. As a result, this improved model achieves an impressive 98.71% accuracy on an openly accessible database, while utilizing only 5.94 million parameters. Compared to the previous method, this maximum improvement reaches 5.41%. Moreover, this research successfully reduces the parameter count by up to 24 times, showcasing the efficacy of our approach. This demonstrates the significant benefits in regions with limited availability of medical resources.
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Affiliation(s)
- Shujuan Wang
- College of Mathematics and Information Science, Zhengzhou University of Light Industry, Zhengzhou, China
| | - Jialin Ren
- College of Mathematics and Information Science, Zhengzhou University of Light Industry, Zhengzhou, China
| | - Xiaoli Guo
- College of Mathematics and Information Science, Zhengzhou University of Light Industry, Zhengzhou, China
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Tariq MU, Ismail SB. AI-powered COVID-19 forecasting: a comprehensive comparison of advanced deep learning methods. Osong Public Health Res Perspect 2024; 15:115-136. [PMID: 38621765 PMCID: PMC11082441 DOI: 10.24171/j.phrp.2023.0287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 01/07/2024] [Accepted: 01/26/2024] [Indexed: 04/17/2024] Open
Abstract
BACKGROUND The coronavirus disease 2019 (COVID-19) pandemic continues to pose significant challenges to the public health sector, including that of the United Arab Emirates (UAE). The objective of this study was to assess the efficiency and accuracy of various deep-learning models in forecasting COVID-19 cases within the UAE, thereby aiding the nation's public health authorities in informed decision-making. METHODS This study utilized a comprehensive dataset encompassing confirmed COVID-19 cases, demographic statistics, and socioeconomic indicators. Several advanced deep learning models, including long short-term memory (LSTM), bidirectional LSTM, convolutional neural network (CNN), CNN-LSTM, multilayer perceptron, and recurrent neural network (RNN) models, were trained and evaluated. Bayesian optimization was also implemented to fine-tune these models. RESULTS The evaluation framework revealed that each model exhibited different levels of predictive accuracy and precision. Specifically, the RNN model outperformed the other architectures even without optimization. Comprehensive predictive and perspective analytics were conducted to scrutinize the COVID-19 dataset. CONCLUSION This study transcends academic boundaries by offering critical insights that enable public health authorities in the UAE to deploy targeted data-driven interventions. The RNN model, which was identified as the most reliable and accurate for this specific context, can significantly influence public health decisions. Moreover, the broader implications of this research validate the capability of deep learning techniques in handling complex datasets, thus offering the transformative potential for predictive accuracy in the public health and healthcare sectors.
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Affiliation(s)
- Muhammad Usman Tariq
- Marketing, Operations, and Information System, Abu Dhabi University, Abu Dhabi, United Arab Emirates
- Faculty of Computer Science and Information Technology, Univesiti Tun Hussien Onn Malaysia, Parit Raja, Malaysia
| | - Shuhaida Binti Ismail
- Faculty of Computer Science and Information Technology, Univesiti Tun Hussien Onn Malaysia, Parit Raja, Malaysia
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Awais M, Abdal MN, Akram T, Alasiry A, Marzougui M, Masood A. An efficient decision support system for leukemia identification utilizing nature-inspired deep feature optimization. Front Oncol 2024; 14:1328200. [PMID: 38505591 PMCID: PMC10949894 DOI: 10.3389/fonc.2024.1328200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Accepted: 01/25/2024] [Indexed: 03/21/2024] Open
Abstract
In the field of medicine, decision support systems play a crucial role by harnessing cutting-edge technology and data analysis to assist doctors in disease diagnosis and treatment. Leukemia is a malignancy that emerges from the uncontrolled growth of immature white blood cells within the human body. An accurate and prompt diagnosis of leukemia is desired due to its swift progression to distant parts of the body. Acute lymphoblastic leukemia (ALL) is an aggressive type of leukemia that affects both children and adults. Computer vision-based identification of leukemia is challenging due to structural irregularities and morphological similarities of blood entities. Deep neural networks have shown promise in extracting valuable information from image datasets, but they have high computational costs due to their extensive feature sets. This work presents an efficient pipeline for binary and subtype classification of acute lymphoblastic leukemia. The proposed method first unveils a novel neighborhood pixel transformation method using differential evolution to improve the clarity and discriminability of blood cell images for better analysis. Next, a hybrid feature extraction approach is presented leveraging transfer learning from selected deep neural network models, InceptionV3 and DenseNet201, to extract comprehensive feature sets. To optimize feature selection, a customized binary Grey Wolf Algorithm is utilized, achieving an impressive 80% reduction in feature size while preserving key discriminative information. These optimized features subsequently empower multiple classifiers, potentially capturing diverse perspectives and amplifying classification accuracy. The proposed pipeline is validated on publicly available standard datasets of ALL images. For binary classification, the best average accuracy of 98.1% is achieved with 98.1% sensitivity and 98% precision. For ALL subtype classifications, the best accuracy of 98.14% was attained with 78.5% sensitivity and 98% precision. The proposed feature selection method shows a better convergence behavior as compared to classical population-based meta-heuristics. The suggested solution also demonstrates comparable or better performance in comparison to several existing techniques.
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Affiliation(s)
- Muhammad Awais
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Wah, Pakistan
- Department of Computer Engineering, TED University, Ankara, Türkiye
| | - Md. Nazmul Abdal
- Department of Computer Science and Engineering, University of Liberal Arts Bangladesh, Dhaka, Bangladesh
| | - Tallha Akram
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Wah, Pakistan
| | - Areej Alasiry
- College of Computer Science, King Khalid University, Abha, Saudi Arabia
| | - Mehrez Marzougui
- College of Computer Science, King Khalid University, Abha, Saudi Arabia
| | - Anum Masood
- Department of Physics, Norwegian University of Science and Technology, Trondheim, Norway
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Cao Y, Feng J, Wang C, Yang F, Wang X, Xu J, Huang C, Zhang S, Li Z, Mao L, Zhang T, Jia B, Li T, Li H, Zhang B, Shi H, Li D, Zhang N, Yu Y, Meng X, Zhang Z. LNAS: a clinically applicable deep-learning system for mediastinal enlarged lymph nodes segmentation and station mapping without regard to the pathogenesis using unenhanced CT images. LA RADIOLOGIA MEDICA 2024; 129:229-238. [PMID: 38108979 DOI: 10.1007/s11547-023-01747-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 10/20/2023] [Indexed: 12/19/2023]
Abstract
BACKGROUND The accurate identification and evaluation of lymph nodes by CT images is of great significance for disease diagnosis, treatment, and prognosis. PURPOSE To assess the lymph nodes' segmentation, size, and station by artificial intelligence (AI) for unenhanced chest CT images and evaluate its value in clinical scenarios. MATERIAL AND METHODS This retrospective study proposed an end-to-end Lymph Nodes Analysis System (LNAS) consisting of three models: the Lymph Node Segmentation model (LNS), the Mediastinal Organ Segmentation model (MOS), and the Lymph Node Station Registration model (LNR). We selected a healthy chest CT image as the template image and annotated 14 lymph node station masks according to the IASLC to build the lymph node station mapping template. The exact contours and stations of the lymph nodes were annotated by two junior radiologists and reviewed by a senior radiologist. Patients aged 18 and above, who had undergone unenhanced chest CT and had at least one suspicious enlarged mediastinal lymph node in imaging reports, were included. Exclusions were patients who had thoracic surgeries in the past 2 weeks or artifacts on CT images affecting lymph node observation by radiologists. The system was trained on 6725 consecutive chest CTs that from Tianjin Medical University General Hospital, among which 6249 patients had suspicious enlarged mediastinal lymph nodes. A total of 519 consecutive chest CTs from Qilu Hospital of Shandong University (Qingdao) were used for external validation. The gold standard for each CT was determined by two radiologists and reviewed by one senior radiologist. RESULTS The patient-level sensitivity of the LNAS system reached of 93.94% and 92.89% in internal and external test dataset, respectively. And the lesion-level sensitivity (recall) reached 89.48% and 85.97% in internal and external test dataset. For man-machine comparison, AI significantly apparently shortened the average reading time (p < 0.001) and had better lesion-level and patient-level sensitivities. CONCLUSION AI improved the sensitivity lymph node segmentation by radiologists with an advantage in reading time.
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Affiliation(s)
- Yang Cao
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Jintang Feng
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
- Department of Radiology, Tianjin Chest Hospital, Tianjin, China
| | | | - Fan Yang
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Xiaomeng Wang
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | | | | | | | | | - Li Mao
- Deepwise AI Lab, Beijing, China
| | - Tianzhu Zhang
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Bingzhen Jia
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Tongli Li
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Hui Li
- Department of Radiology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Bingjin Zhang
- Department of Radiology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Hongmei Shi
- Department of Radiology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Dong Li
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Ningnannan Zhang
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Yizhou Yu
- Deepwise AI Lab, Beijing, China
- Department of Computer Science, The University of Hong Kong, Hong Kong, China
| | - Xiangshui Meng
- Department of Radiology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Zhang Zhang
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China.
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Saha S, Nandi D. SVM-RLF-DNN: A DNN with reliefF and SVM for automatic identification of COVID from chest X-ray and CT images. Digit Health 2024; 10:20552076241257045. [PMID: 38812845 PMCID: PMC11135098 DOI: 10.1177/20552076241257045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Accepted: 05/08/2024] [Indexed: 05/31/2024] Open
Abstract
Aim To develop an advanced determination technology for detecting COVID-19 patterns from chest X-ray and CT-scan films with distinct applications of deep learning and machine learning methods. Methods and Materials The newly enhanced proposed hybrid classification network (SVM-RLF-DNN) comprises of three phases: feature extraction, selection and classification. The in-depth features are extracted from a series of 3×3 convolution, 2×2 max polling operations followed by a flattened and fully connected layer of the deep neural network (DNN). ReLU activation function and Adam optimizer are used in the model. The ReliefF is an improved feature selection algorithm of Relief that uses Manhattan distance instead of Euclidean distance. Based on the significance of the feature, the ReliefF assigns weight to each extracted feature received from a fully connected layer. The weight to each feature is the average of k closest hits and misses in each class for a neighbouring instance pair in multiclass problems. The ReliefF eliminates lower-weight features by setting the node value to zero. The higher weights of the features are kept to obtain the feature selection. At the last layer of the neural network, the multiclass Support Vector Machine (SVM) is used to classify the patterns of COVID-19, viral pneumonia and healthy cases. The three classes with three binary SVM classifiers use linear kernel function for each binary SVM following a one-versus-all approach. The hinge loss function and L2-norm regularization are selected for more stable results. The proposed method is assessed on publicly available chest X-ray and CT-scan image databases from Kaggle and GitHub. The performance of the proposed classification model has comparable training, validation, and test accuracy, as well as sensitivity, specificity, and confusion matrix for quantitative evaluation on five-fold cross-validation. Results Our proposed network has achieved test accuracy of 98.48% and 95.34% on 2-class X-rays and CT. More importantly, the proposed model's test accuracy, sensitivity, and specificity are 87.9%, 86.32%, and 90.25% for 3-class classification (COVID-19, Pneumonia, Normal) on chest X-rays. The proposed model provides the test accuracy, sensitivity, and specificity of 95.34%, 94.12%, and 96.15% for 2-class classification (COVID-19, Non-COVID) on chest CT. Conclusion Our proposed classification network experimental results indicate competitiveness with existing neural networks. The proposed neural network assists clinicians in determining and surveilling the disease.
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Affiliation(s)
- Sanjib Saha
- Department of Computer Science and Engineering, National Institute of Technology, Durgapur, India
- Department of Computer Science and Engineering, Dr. B. C. Roy Engineering College, Durgapur, India
| | - Debashis Nandi
- Department of Computer Science and Engineering, National Institute of Technology, Durgapur, India
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Talukder MA, Layek MA, Kazi M, Uddin MA, Aryal S. Empowering COVID-19 detection: Optimizing performance through fine-tuned EfficientNet deep learning architecture. Comput Biol Med 2024; 168:107789. [PMID: 38042105 DOI: 10.1016/j.compbiomed.2023.107789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 11/21/2023] [Accepted: 11/28/2023] [Indexed: 12/04/2023]
Abstract
The worldwide COVID-19 pandemic has profoundly influenced the health and everyday experiences of individuals across the planet. It is a highly contagious respiratory disease requiring early and accurate detection to curb its rapid transmission. Initial testing methods primarily revolved around identifying the genetic composition of the coronavirus, exhibiting a relatively low detection rate and requiring a time-intensive procedure. To address this challenge, experts have suggested using radiological imagery, particularly chest X-rays, as a valuable approach within the diagnostic protocol. This study investigates the potential of leveraging radiographic imaging (X-rays) with deep learning algorithms to swiftly and precisely identify COVID-19 patients. The proposed approach elevates the detection accuracy by fine-tuning with appropriate layers on various established transfer learning models. The experimentation was conducted on a COVID-19 X-ray dataset containing 2000 images. The accuracy rates achieved were impressive of 99.55%, 97.32%, 99.11%, 99.55%, 99.11% and 100% for Xception, InceptionResNetV2, ResNet50 , ResNet50V2, EfficientNetB0 and EfficientNetB4 respectively. The fine-tuned EfficientNetB4 achieved an excellent accuracy score, showcasing its potential as a robust COVID-19 detection model. Furthermore, EfficientNetB4 excelled in identifying Lung disease using Chest X-ray dataset containing 4,350 Images, achieving remarkable performance with an accuracy of 99.17%, precision of 99.13%, recall of 99.16%, and f1-score of 99.14%. These results highlight the promise of fine-tuned transfer learning for efficient lung detection through medical imaging, especially with X-ray images. This research offers radiologists an effective means of aiding rapid and precise COVID-19 diagnosis and contributes valuable assistance for healthcare professionals in accurately identifying affected patients.
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Affiliation(s)
- Md Alamin Talukder
- Department of Computer Science and Engineering, Jagannath University, Dhaka, Bangladesh.
| | - Md Abu Layek
- Department of Computer Science and Engineering, Jagannath University, Dhaka, Bangladesh.
| | - Mohsin Kazi
- Department of Pharmaceutics, College of Pharmacy, King Saud University, P.O. Box-2457, Riyadh 11451, Saudi Arabia.
| | - Md Ashraf Uddin
- School of Information Technology, Deakin University, Waurn Ponds Campus, Geelong, Australia.
| | - Sunil Aryal
- School of Information Technology, Deakin University, Waurn Ponds Campus, Geelong, Australia.
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Guo K, Cheng J, Li K, Wang L, Lv Y, Cao D. Diagnosis and detection of pneumonia using weak-label based on X-ray images: a multi-center study. BMC Med Imaging 2023; 23:209. [PMID: 38087255 PMCID: PMC10717871 DOI: 10.1186/s12880-023-01174-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 12/05/2023] [Indexed: 12/18/2023] Open
Abstract
PURPOSE Development and assessment the deep learning weakly supervised algorithm for the classification and detection pneumonia via X-ray. METHODS This retrospective study analyzed two publicly available dataset that contain X-ray images of pneumonia cases and normal cases. The first dataset from Guangzhou Women and Children's Medical Center. It contains a total of 5,856 X-ray images, which are divided into training, validation, and test sets with 8:1:1 ratio for algorithm training and testing. The deep learning algorithm ResNet34 was employed to build diagnostic model. And the second public dataset were collated by researchers from Qatar University and the University of Dhaka along with collaborators from Pakistan and Malaysia and some medical doctors. A total of 1,300 images of COVID-19 positive cases, 1,300 normal images and 1,300 images of viral pneumonia for external validation. Class activation map (CAM) were used to location the pneumonia lesions. RESULTS The ResNet34 model for pneumonia detection achieved an AUC of 0.9949 [0.9910-0.9981] (with an accuracy of 98.29% a sensitivity of 99.29% and a specificity of 95.57%) in the test dataset. And for external validation dataset, the model obtained an AUC of 0.9835[0.9806-0.9864] (with an accuracy of 94.62%, a sensitivity of 92.35% and a specificity of 99.15%). Moreover, the CAM can accurately locate the pneumonia area. CONCLUSION The deep learning algorithm can accurately detect pneumonia and locate the pneumonia area based on weak supervision information, which can provide potential value for helping radiologists to improve their accuracy of detection pneumonia patients through X-ray images.
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Affiliation(s)
- Kairou Guo
- Department of Biomedical Engineering, Chinese PLA General Hospital, Beijing, 100853, P.R. China
| | - Jiangbo Cheng
- Department of Biomedical Engineering, Chinese PLA General Hospital, Beijing, 100853, P.R. China
| | - Kaiyuan Li
- Department of Biomedical Engineering, Chinese PLA General Hospital, Beijing, 100853, P.R. China
| | - Lanhui Wang
- Department of Biomedical Engineering, Chinese PLA General Hospital, Beijing, 100853, P.R. China
| | - Yadong Lv
- Department of Biomedical Engineering, Chinese PLA General Hospital, Beijing, 100853, P.R. China
| | - Desen Cao
- Department of Biomedical Engineering, Chinese PLA General Hospital, Beijing, 100853, P.R. China.
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Faruqui N, Yousuf MA, Kateb FA, Abdul Hamid M, Monowar MM. Healthcare As a Service (HAAS): CNN-based cloud computing model for ubiquitous access to lung cancer diagnosis. Heliyon 2023; 9:e21520. [PMID: 37942151 PMCID: PMC10628703 DOI: 10.1016/j.heliyon.2023.e21520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Revised: 09/27/2023] [Accepted: 10/23/2023] [Indexed: 11/10/2023] Open
Abstract
The field of automated lung cancer diagnosis using Computed Tomography (CT) scans has been significantly advanced by the precise predictions offered by Convolutional Neural Network (CNN)-based classifiers. Critical areas of study include improving image quality, optimizing learning algorithms, and enhancing diagnostic accuracy. To facilitate a seamless transition from research laboratories to real-world applications, it is crucial to improve the technology's usability-a factor often neglected in current state-of-the-art research. Yet, current state-of-the-art research in this field frequently overlooks the need for expediting this process. This paper introduces Healthcare-As-A-Service (HAAS), an innovative concept inspired by Software-As-A-Service (SAAS) within the cloud computing paradigm. As a comprehensive lung cancer diagnosis service system, HAAS has the potential to reduce lung cancer mortality rates by providing early diagnosis opportunities to everyone. We present HAASNet, a cloud-compatible CNN that boasts an accuracy rate of 96.07%. By integrating HAASNet predictions with physio-symptomatic data from the Internet of Medical Things (IoMT), the proposed HAAS model generates accurate and reliable lung cancer diagnosis reports. Leveraging IoMT and cloud technology, the proposed service is globally accessible via the Internet, transcending geographic boundaries. This groundbreaking lung cancer diagnosis service achieves average precision, recall, and F1-scores of 96.47%, 95.39%, and 94.81%, respectively.
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Affiliation(s)
- Nuruzzaman Faruqui
- Institute of Information Technology (IIT), Jahangirnagar University, Savar, Dhaka, 1342, Bangladesh
- Department of Software Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka, 1216, Bangladesh
| | - Mohammad Abu Yousuf
- Institute of Information Technology (IIT), Jahangirnagar University, Savar, Dhaka, 1342, Bangladesh
| | - Faris A. Kateb
- Department of Information Technology, Faculty of Computing & Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
| | - Md. Abdul Hamid
- Department of Information Technology, Faculty of Computing & Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
| | - Muhammad Mostafa Monowar
- Department of Information Technology, Faculty of Computing & Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
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Patro KK, Allam JP, Sanapala U, Marpu CK, Samee NA, Alabdulhafith M, Plawiak P. An effective correlation-based data modeling framework for automatic diabetes prediction using machine and deep learning techniques. BMC Bioinformatics 2023; 24:372. [PMID: 37784049 PMCID: PMC10544445 DOI: 10.1186/s12859-023-05488-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 09/19/2023] [Indexed: 10/04/2023] Open
Abstract
The rising risk of diabetes, particularly in emerging countries, highlights the importance of early detection. Manual prediction can be a challenging task, leading to the need for automatic approaches. The major challenge with biomedical datasets is data scarcity. Biomedical data is often difficult to obtain in large quantities, which can limit the ability to train deep learning models effectively. Biomedical data can be noisy and inconsistent, which can make it difficult to train accurate models. To overcome the above-mentioned challenges, this work presents a new framework for data modeling that is based on correlation measures between features and can be used to process data effectively for predicting diabetes. The standard, publicly available Pima Indians Medical Diabetes (PIMA) dataset is utilized to verify the effectiveness of the proposed techniques. Experiments using the PIMA dataset showed that the proposed data modeling method improved the accuracy of machine learning models by an average of 9%, with deep convolutional neural network models achieving an accuracy of 96.13%. Overall, this study demonstrates the effectiveness of the proposed strategy in the early and reliable prediction of diabetes.
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Affiliation(s)
- Kiran Kumar Patro
- Department of ECE, Aditya Institute of Technology and Management, Tekkali, AP, 532201, India
| | - Jaya Prakash Allam
- School of Computer Science and Engineering, VIT Vellore, Katpadi, Vellore, Tamil Nadu, 632014, India.
| | | | - Chaitanya Kumar Marpu
- Department of ECE, Aditya Institute of Technology and Management, Tekkali, AP, 532201, India
| | - Nagwan Abdel Samee
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
| | - Maali Alabdulhafith
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
| | - Pawel Plawiak
- Department of Computer Science, Faculty of Computer Science and Telecommunications, Cracow University of Technology, Warszawska 24, 31-155, Krakow, Poland
- Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Bałtycka 5, 44-100, Gliwice, Poland
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11
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Hasan MM, Hossain MM, Rahman MM, Azad A, Alyami SA, Moni MA. FP-CNN: Fuzzy pooling-based convolutional neural network for lung ultrasound image classification with explainable AI. Comput Biol Med 2023; 165:107407. [PMID: 37678140 DOI: 10.1016/j.compbiomed.2023.107407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 08/08/2023] [Accepted: 08/26/2023] [Indexed: 09/09/2023]
Abstract
The COVID-19 pandemic wreaks havoc on healthcare systems all across the world. In pandemic scenarios like COVID-19, the applicability of diagnostic modalities is crucial in medical diagnosis, where non-invasive ultrasound imaging has the potential to be a useful biomarker. This research develops a computer-assisted intelligent methodology for ultrasound lung image classification by utilizing a fuzzy pooling-based convolutional neural network FP-CNN with underlying evidence of particular decisions. The fuzzy-pooling method finds better representative features for ultrasound image classification. The FPCNN model categorizes ultrasound images into one of three classes: covid, disease-free (normal), and pneumonia. Explanations of diagnostic decisions are crucial to ensure the fairness of an intelligent system. This research has used Shapley Additive Explanation (SHAP) to explain the prediction of the FP-CNN models. The prediction of the black-box model is illustrated using the SHAP explanation of the intermediate layers of the black-box model. To determine the most effective model, we have tested different state-of-the-art convolutional neural network architectures with various training strategies, including fine-tuned models, single-layer fuzzy pooling models, and fuzzy pooling at all pooling layers. Among different architectures, the Xception model with all pooling layers having fuzzy pooling achieves the best classification results of 97.2% accuracy. We hope our proposed method will be helpful for the clinical diagnosis of covid-19 from lung ultrasound (LUS) images.
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Affiliation(s)
- Md Mahmodul Hasan
- Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Tangail, 1902, Dhaka, Bangladesh.
| | - Muhammad Minoar Hossain
- Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Tangail, 1902, Dhaka, Bangladesh; Department of Computer Science and Engineering, Bangladesh University, Mohammadpur, Dhaka, 1207, Bangladesh.
| | - Mohammad Motiur Rahman
- Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Tangail, 1902, Dhaka, Bangladesh.
| | - Akm Azad
- Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 13318, Saudi Arabia.
| | - Salem A Alyami
- Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 13318, Saudi Arabia.
| | - Mohammad Ali Moni
- Artificial Intelligence & Data Science, School of Health and Rehabilitation Sciences, The University of Queensland, Brisbane, QLD 4072, Australia; Artificial Intelligence and Cyber Futures Institute, Charles Stuart University, Bathurst, NSW 2795, Australia.
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12
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Huang ML, Liao YC. Stacking Ensemble and ECA-EfficientNetV2 Convolutional Neural Networks on Classification of Multiple Chest Diseases Including COVID-19. Acad Radiol 2023; 30:1915-1935. [PMID: 36526533 PMCID: PMC9748720 DOI: 10.1016/j.acra.2022.11.027] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 11/15/2022] [Accepted: 11/20/2022] [Indexed: 11/27/2022]
Abstract
RATIONALE AND OBJECTIVES Early detection and treatment of COVID-19 patients is crucial. Convolutional neural networks have been proven to accurately extract features in medical images, which accelerates time required for testing and increases the effectiveness of COVID-19 diagnosis. This study proposes two classification models for multiple chest diseases including COVID-19. MATERIALS AND METHODS The first is Stacking-ensemble model, which stacks six pretrained models including EfficientNetV2-B0, EfficientNetV2-B1, EfficientNetV2-B2, EfficientNetV2-B3, EfficientNetV2-S and EfficientNetV2-M. The second model is self-designed model ECA-EfficientNetV2 based on ECA-Net and EfficientNetV2. Ten-fold cross validation was performed for each model on chest X-ray and CT images. One more dataset, COVID-CT dataset, was tested to verify the performance of the proposed Stacking-ensemble and ECA-EfficientNetV2 models. RESULTS The best performance comes from the proposed ECA-EfficientNetV2 model with the highest Accuracy of 99.21%, Precision of 99.23%, Recall of 99.25%, F1-score of 99.20%, and (area under the curve) AUC of 99.51% on chest X-ray dataset; the best performance comes from the proposed ECA-EfficientNetV2 model with the highest Accuracy of 99.81%, Precision of 99.80%, Recall of 99.80%, F1-score of 99.81%, and AUC of 99.87% on chest CT dataset. The differences for five metrics between Stacking-ensemble and ECA-EfficientNetV2 models are not significant. CONCLUSION Ensemble model achieves better performance than single pretrained models. Compared to the SOTA, Stacking-ensemble and ECA-EfficientNetV2 models proposed in this study demonstrate promising performance on classification of multiple chest diseases including COVID-19.
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Affiliation(s)
- Mei-Ling Huang
- Department of Industrial Engineering & Management, National Chin-Yi University of Technology, 57, Sec. 2, Zhongshan Rd., Taiping Dist., Taichung, 411030, Taiwan.
| | - Yu-Chieh Liao
- Department of Industrial Engineering & Management, National Chin-Yi University of Technology, 57, Sec. 2, Zhongshan Rd., Taiping Dist., Taichung, 411030, Taiwan
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13
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Santosh KC, GhoshRoy D, Nakarmi S. A Systematic Review on Deep Structured Learning for COVID-19 Screening Using Chest CT from 2020 to 2022. Healthcare (Basel) 2023; 11:2388. [PMID: 37685422 PMCID: PMC10486542 DOI: 10.3390/healthcare11172388] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 08/16/2023] [Accepted: 08/22/2023] [Indexed: 09/10/2023] Open
Abstract
The emergence of the COVID-19 pandemic in Wuhan in 2019 led to the discovery of a novel coronavirus. The World Health Organization (WHO) designated it as a global pandemic on 11 March 2020 due to its rapid and widespread transmission. Its impact has had profound implications, particularly in the realm of public health. Extensive scientific endeavors have been directed towards devising effective treatment strategies and vaccines. Within the healthcare and medical imaging domain, the application of artificial intelligence (AI) has brought significant advantages. This study delves into peer-reviewed research articles spanning the years 2020 to 2022, focusing on AI-driven methodologies for the analysis and screening of COVID-19 through chest CT scan data. We assess the efficacy of deep learning algorithms in facilitating decision making processes. Our exploration encompasses various facets, including data collection, systematic contributions, emerging techniques, and encountered challenges. However, the comparison of outcomes between 2020 and 2022 proves intricate due to shifts in dataset magnitudes over time. The initiatives aimed at developing AI-powered tools for the detection, localization, and segmentation of COVID-19 cases are primarily centered on educational and training contexts. We deliberate on their merits and constraints, particularly in the context of necessitating cross-population train/test models. Our analysis encompassed a review of 231 research publications, bolstered by a meta-analysis employing search keywords (COVID-19 OR Coronavirus) AND chest CT AND (deep learning OR artificial intelligence OR medical imaging) on both the PubMed Central Repository and Web of Science platforms.
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Affiliation(s)
- KC Santosh
- 2AI: Applied Artificial Intelligence Research Lab, Vermillion, SD 57069, USA
| | - Debasmita GhoshRoy
- School of Automation, Banasthali Vidyapith, Tonk 304022, Rajasthan, India;
| | - Suprim Nakarmi
- Department of Computer Science, University of South Dakota, Vermillion, SD 57069, USA;
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Wali A, Ahmad M, Naseer A, Tamoor M, Gilani S. StynMedGAN: Medical images augmentation using a new GAN model for improved diagnosis of diseases. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023. [DOI: 10.3233/jifs-223996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/07/2023]
Abstract
Deep networks require a considerable amount of training data otherwise these networks generalize poorly. Data Augmentation techniques help the network generalize better by providing more variety in the training data. Standard data augmentation techniques such as flipping, and scaling, produce new data that is a modified version of the original data. Generative Adversarial networks (GANs) have been designed to generate new data that can be exploited. In this paper, we propose a new GAN model, named StynMedGAN for synthetically generating medical images to improve the performance of classification models. StynMedGAN builds upon the state-of-the-art styleGANv2 that has produced remarkable results generating all kinds of natural images. We introduce a regularization term that is a normalized loss factor in the existing discriminator loss of styleGANv2. It is used to force the generator to produce normalized images and penalize it if it fails. Medical imaging modalities, such as X-Rays, CT-Scans, and MRIs are different in nature, we show that the proposed GAN extends the capacity of styleGANv2 to handle medical images in a better way. This new GAN model (StynMedGAN) is applied to three types of medical imaging: X-Rays, CT scans, and MRI to produce more data for the classification tasks. To validate the effectiveness of the proposed model for the classification, 3 classifiers (CNN, DenseNet121, and VGG-16) are used. Results show that the classifiers trained with StynMedGAN-augmented data outperform other methods that only used the original data. The proposed model achieved 100%, 99.6%, and 100% for chest X-Ray, Chest CT-Scans, and Brain MRI respectively. The results are promising and favor a potentially important resource that can be used by practitioners and radiologists to diagnose different diseases.
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Affiliation(s)
- Aamir Wali
- Department of Computer Science, National University of Computer and Emerging Science, Faisal Town, Lahore, Pakistan
| | - Muzammil Ahmad
- Department of Computer Science, National University of Computer and Emerging Science, Faisal Town, Lahore, Pakistan
| | - Asma Naseer
- Department of Computer Science, National University of Computer and Emerging Science, Faisal Town, Lahore, Pakistan
| | - Maria Tamoor
- Department of Computer Science, Forman Christian College University, Zahoor Ilahi Road, Lahore, Pakistan
| | - S.A.M. Gilani
- Department of Computer Science, National University of Computer and Emerging Science, Faisal Town, Lahore, Pakistan
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15
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COVID-19 and human development: An approach for classification of HDI with deep CNN. Biomed Signal Process Control 2023; 81:104499. [PMID: 36530217 PMCID: PMC9742222 DOI: 10.1016/j.bspc.2022.104499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 10/18/2022] [Accepted: 12/01/2022] [Indexed: 12/14/2022]
Abstract
The measures taken during the pandemic have had lasting effects on people's lives and perceptions of the ability of national and multilateral institutions to drive human development. Policies that changed people's behavior were at the heart of containing the spread of the virus. As a result, it has become a systemic human development crisis affecting health, the economy, education, social life, and accumulated gains. This study shows how the relationship of the Human Development Index (HDI), which has combined effects on health, education, and the economy, should be considered in the context of pandemic factors. First, COVID-19 data of the countries received from a public and credible source were extracted and organized into an acceptable structure. Then, we applied statistical feature selection to determine which variables are closely related to HDI and enabled the Deep Convolutional Neural Network (DCNN) model to give more accurate results. The Continuous Wavelet Transform (CWT) and scalogram methods were used for the time-series data visualization. Three different images of each country are combined into a single image to penetrate each other for ease of processing. These images were made suitable for the input of the ResNet-50 network, which is a pre-trained DCNN model, by going through various preprocessing processes. After the training and validation processes, the feature vectors in the fc1000 layer of the network were drawn and given to the Support Vector Machine Classifier (SVMC) input. We achieved total performance metrics of specificity (88.2%), sensitivity (96.5%), precision (99%), F1 Score (94.9%) and MCC (85.9%).
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16
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Effects of image-sharpening algorithm on surgical field visibility during 3D heads-up surgery for vitreoretinal diseases. Sci Rep 2023; 13:2758. [PMID: 36797311 PMCID: PMC9935873 DOI: 10.1038/s41598-023-29882-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 02/11/2023] [Indexed: 02/18/2023] Open
Abstract
We conducted clinical and experimental studies to investigate the effects of image-sharpening algorithms and color adjustments, which enabled real-time processing of live surgical images with a delay of 0.004 s. The images were processed with image-sharpening intensities of 0%, 12.5%, 25%, and 50% during cataract surgery, vitrectomy, peeling of epiretinal membrane, and peeling of internal limiting membrane (ILM) with the Ngenuity 3D visualization system. In addition, the images obtained with a yellow filter during the ILM peeling were processed with color adjustments. Five vitreoretinal surgeons scored the clarity of the images on a 10-point scale. The images of a 1951 United States Air Force grating target placed in no fluid (control), saline, and 0.1% and 1% milk solution were evaluated. The results showed that the mean visibility score increased significantly from 5.0 ± 0.6 at 0% to 6.4 ± 0.6 at 12.5%, 7.3 ± 0.7 at 25%, and 7.5 ± 0.9 at 50% (P < 0.001). The visibility scores during ILM peeling improved significantly with color adjustments (P = 0.005). In the experimental study, the contrast of the grating targets blurred by the 0.1% and 1% milk solution increased significantly by the image-sharpening procedure. We conclude that the image-sharpening algorithms and color adjustments improved the intraoperative visibility of 3D heads-up surgery.
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17
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Ahamed MKU, Islam MM, Uddin MA, Akhter A, Acharjee UK, Paul BK, Moni MA. DTLCx: An Improved ResNet Architecture to Classify Normal and Conventional Pneumonia Cases from COVID-19 Instances with Grad-CAM-Based Superimposed Visualization Utilizing Chest X-ray Images. Diagnostics (Basel) 2023; 13:diagnostics13030551. [PMID: 36766662 PMCID: PMC9914155 DOI: 10.3390/diagnostics13030551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 01/04/2023] [Accepted: 01/31/2023] [Indexed: 02/05/2023] Open
Abstract
COVID-19 is a severe respiratory contagious disease that has now spread all over the world. COVID-19 has terribly impacted public health, daily lives and the global economy. Although some developed countries have advanced well in detecting and bearing this coronavirus, most developing countries are having difficulty in detecting COVID-19 cases for the mass population. In many countries, there is a scarcity of COVID-19 testing kits and other resources due to the increasing rate of COVID-19 infections. Therefore, this deficit of testing resources and the increasing figure of daily cases encouraged us to improve a deep learning model to aid clinicians, radiologists and provide timely assistance to patients. In this article, an efficient deep learning-based model to detect COVID-19 cases that utilizes a chest X-ray images dataset has been proposed and investigated. The proposed model is developed based on ResNet50V2 architecture. The base architecture of ResNet50V2 is concatenated with six extra layers to make the model more robust and efficient. Finally, a Grad-CAM-based discriminative localization is used to readily interpret the detection of radiological images. Two datasets were gathered from different sources that are publicly available with class labels: normal, confirmed COVID-19, bacterial pneumonia and viral pneumonia cases. Our proposed model obtained a comprehensive accuracy of 99.51% for four-class cases (COVID-19/normal/bacterial pneumonia/viral pneumonia) on Dataset-2, 96.52% for the cases with three classes (normal/ COVID-19/bacterial pneumonia) and 99.13% for the cases with two classes (COVID-19/normal) on Dataset-1. The accuracy level of the proposed model might motivate radiologists to rapidly detect and diagnose COVID-19 cases.
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Affiliation(s)
- Md. Khabir Uddin Ahamed
- Department of Computer Science and Engineering, Jagannath University, Dhaka 1100, Bangladesh
| | - Md Manowarul Islam
- Department of Computer Science and Engineering, Jagannath University, Dhaka 1100, Bangladesh
- Correspondence:
| | - Md. Ashraf Uddin
- Department of Computer Science and Engineering, Jagannath University, Dhaka 1100, Bangladesh
- School of Information Technology, Geelong, Deakin University, Geelong, VIC 3216, Australia
| | - Arnisha Akhter
- Department of Computer Science and Engineering, Jagannath University, Dhaka 1100, Bangladesh
| | - Uzzal Kumar Acharjee
- Department of Computer Science and Engineering, Jagannath University, Dhaka 1100, Bangladesh
| | - Bikash Kumar Paul
- Department of Information and Communication Technology, Mawlana Bhashani Science and Technology University, Tangail 1902, Bangladesh
- Department of Software Engineering, Daffodil International University, Dhaka 1207, Bangladesh
| | - Mohammad Ali Moni
- Artificial Intelligence & Data Science, School of Health and Rehabilitation Sciences, Faculty of Health and Behavioural Sciences, The University of Queensland, St. Lucia, QLD 4072, Australia
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18
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Talukder MA, Hasan KF, Islam MM, Uddin MA, Akhter A, Yousuf MA, Alharbi F, Moni MA. A dependable hybrid machine learning model for network intrusion detection. JOURNAL OF INFORMATION SECURITY AND APPLICATIONS 2023. [DOI: 10.1016/j.jisa.2022.103405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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19
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Xu F, Zhu C, Wang Z, Zhang L, Gao H, Ma Z, Gao Y, Guo Y, Li X, Luo Y, Li M, Shen G, Liu H, Li Y, Zhang C, Cui J, Li J, Jiang H, Liu J. Deep learning for real-time detection of breast cancer presenting pathological nipple discharge by ductoscopy. Front Oncol 2023; 13:1103145. [PMID: 37035165 PMCID: PMC10073663 DOI: 10.3389/fonc.2023.1103145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Accepted: 02/22/2023] [Indexed: 04/11/2023] Open
Abstract
Objective As a common breast cancer-related complaint, pathological nipple discharge (PND) detected by ductoscopy is often missed diagnosed. Deep learning techniques have enabled great advances in clinical imaging but are rarely applied in breast cancer with PND. This study aimed to design and validate an Intelligent Ductoscopy for Breast Cancer Diagnostic System (IDBCS) for breast cancer diagnosis by analyzing real-time imaging data acquired by ductoscopy. Materials and methods The present multicenter, case-control trial was carried out in 6 hospitals in China. Images for consecutive patients, aged ≥18 years, with no previous ductoscopy, were obtained from the involved hospitals. All individuals with PND confirmed from breast lesions by ductoscopy were eligible. Images from Beijing Chao-Yang Hospital were randomly assigned (8:2) to the training (IDBCS development) and internal validation (performance evaluation of the IDBCS) datasets. Diagnostic performance was further assessed with internal and prospective validation datasets from Beijing Chao-Yang Hospital; further external validation was carried out with datasets from 5 primary care hospitals. Diagnostic accuracies, sensitivities, specificities, and positive and negative predictive values for IDBCS and endoscopists (expert, competent, or trainee) in the detection of malignant lesions were obtained by the Clopper-Pearson method. Results Totally 11305 ductoscopy images in 1072 patients were utilized for developing and testing the IDBCS. Area under the curves (AUCs) in breast cancer detection were 0·975 (95%CI 0·899-0·998) and 0·954 (95%CI 0·925-0·975) in the internal validation and prospective datasets, respectively, and ranged between 0·922 (95%CI 0·866-0·960) and 0·965 (95%CI 0·892-0·994) in the 5 external validation datasets. The IDBCS had superior diagnostic accuracy compared with expert (0.912 [95%CI 0.839-0.959] vs 0.726 [0.672-0.775]; p<0.001), competent (0.699 [95%CI 0.645-0.750], p<0.001), and trainee (0.703 [95%CI 0.648-0.753], p<0.001) endoscopists. Conclusions IDBCS outperforms clinical oncologists, achieving high accuracy in diagnosing breast cancer with PND. The novel system could help endoscopists improve their diagnostic efficacy in breast cancer diagnosis.
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Affiliation(s)
- Feng Xu
- Department of Breast Surgery, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
- *Correspondence: Feng Xu, ; Chuang Zhu, ; Hongchuan Jiang, ; Jun Liu,
| | - Chuang Zhu
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
- *Correspondence: Feng Xu, ; Chuang Zhu, ; Hongchuan Jiang, ; Jun Liu,
| | - Zhihao Wang
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Lei Zhang
- Department of Breast Surgery, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Haifeng Gao
- Breast Disease Prevention and Treatment Center, Haidian Maternal and Child Health Hospital, Beijing, China
| | - Zhenhai Ma
- Department of General Surgery , Beijing Huairou Hospital, Beijing, China
| | - Yue Gao
- Department of General Surgery , Beijing Huairou Hospital, Beijing, China
| | - Yang Guo
- Department of Breast Surgery, Beijing Yanqing District Maternal and Child Health Care Hospital, Beijing, China
| | - Xuewen Li
- Department of General Surgery, Beijing Pinggu Hospital, Beijing, China
| | - Yunzhao Luo
- Department of Breast Surgery, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Mengxin Li
- Department of Breast Surgery, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Guangqian Shen
- Department of Breast Surgery, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - He Liu
- Department of Breast Surgery, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Yanshuang Li
- Department of Breast Surgery, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Chao Zhang
- Department of Breast Surgery, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Jianxiu Cui
- Department of Breast Surgery, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Jie Li
- Department of Breast Surgery, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Hongchuan Jiang
- Department of Breast Surgery, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
- *Correspondence: Feng Xu, ; Chuang Zhu, ; Hongchuan Jiang, ; Jun Liu,
| | - Jun Liu
- Department of Breast Surgery, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
- *Correspondence: Feng Xu, ; Chuang Zhu, ; Hongchuan Jiang, ; Jun Liu,
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Celik G. Detection of Covid-19 and other pneumonia cases from CT and X-ray chest images using deep learning based on feature reuse residual block and depthwise dilated convolutions neural network. Appl Soft Comput 2023; 133:109906. [PMID: 36504726 PMCID: PMC9726212 DOI: 10.1016/j.asoc.2022.109906] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Revised: 11/29/2022] [Accepted: 12/01/2022] [Indexed: 12/12/2022]
Abstract
Covid-19 has become a worldwide epidemic which has caused the death of millions in a very short time. This disease, which is transmitted rapidly, has mutated and different variations have emerged. Early diagnosis is important to prevent the spread of this disease. In this study, a new deep learning-based architecture is proposed for rapid detection of Covid-19 and other symptoms using CT and X-ray chest images. This method, called CovidDWNet, is based on a structure based on feature reuse residual block (FRB) and depthwise dilated convolutions (DDC) units. The FRB and DDC units efficiently acquired various features in the chest scan images and it was seen that the proposed architecture significantly improved its performance. In addition, the feature maps obtained with the CovidDWNet architecture were estimated with the Gradient boosting (GB) algorithm. With the CovidDWNet+GB architecture, which is a combination of CovidDWNet and GB, a performance increase of approximately 7% in CT images and between 3% and 4% in X-ray images has been achieved. The CovidDWNet+GB architecture achieved the highest success compared to other architectures, with 99.84% and 100% accuracy rates, respectively, on different datasets containing binary class (Covid-19 and Normal) CT images. Similarly, the proposed architecture showed the highest success with 96.81% accuracy in multi-class (Covid-19, Lung Opacity, Normal and Viral Pneumonia) X-ray images and 96.32% accuracy in the dataset containing X-ray and CT images. When the time to predict the disease in CT or X-ray images is examined, it is possible to say that it has a high speed because the CovidDWNet+GB method predicts thousands of images within seconds.
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Affiliation(s)
- Gaffari Celik
- Agri Ibrahim Cecen University, Department of Computer Technology, Agri, Turkey
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Zhou D, Zhu F, Huang ZH, Zhang H, Fan LQ, Fan JY. SPOC domain-containing protein 1 regulates the proliferation and apoptosis of human spermatogonial stem cells through adenylate kinase 4. World J Stem Cells 2022; 14:822-838. [PMID: 36619695 PMCID: PMC9813840 DOI: 10.4252/wjsc.v14.i12.822] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 10/07/2022] [Accepted: 11/30/2022] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Spermatogonial stem cells (SSCs) are the origin of male spermatogenesis, which can reconstruct germ cell lineage in mice. However, the application of SSCs for male fertility restoration is hindered due to the unclear mechanisms of proliferation and self-renewal in humans.
AIM To investigate the role and mechanism of SPOC domain-containing protein 1 (SPOCD1) in human SSC proliferation.
METHODS We analyzed publicly available human testis single-cell RNA sequencing (RNA-seq) data and found that SPOCD1 is predominantly expressed in SSCs in the early developmental stages. Small interfering RNA was applied to suppress SPOCD1 expression to detect the impacts of SPOCD1 inhibition on SSC proliferation and apoptosis. Subsequently, we explored the target genes of SPOCD1 using RNA-seq and confirmed their role by restoring the expression of the target genes. In addition, we examined SPOCD1 expression in some non-obstructive azoospermia (NOA) patients to explore the correlation between SPOCD1 and NOA.
RESULTS The uniform manifold approximation and projection clustering and pseudotime analysis showed that SPOCD1 was highly expressed in the early stages of SSC, and immunohistological results showed that SPOCD1 was mainly localized in glial cell line-derived neurotrophic factor family receptor alpha-1 positive SSCs. SPOCD1 knockdown significantly inhibited cell proliferation and promoted apoptosis. RNA-seq results showed that SPOCD1 knockdown significantly downregulated genes such as adenylate kinase 4 (AK4). Overexpression of AK4 in SPOCD1 knockdown cells partially reversed the phenotypic changes, indicating that AK4 is a functional target gene of SPOCD1. In addition, we found a significant downregulation of SPOCD1 expression in some NOA patients, suggesting that the downregulation of SPOCD1 may be relevant for NOA.
CONCLUSION Our study broadens the understanding of human SSC fate determination and may offer new theories on the etiology of male infertility.
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Affiliation(s)
- Dai Zhou
- Institute of Reproduction and Stem Cell Engineering, School of Basic Medicine Science, Central South University, Changsha 410000, Hunan Province, China
- Reproductive and Genetic Hospital of CITIC-Xiangya, Changsha 410000, Hunan Province, China
- College of Life Sciences, Hunan Normal University, Changsha 410000, Hunan Province, China
- Clinical Research Center for Reproduction and Genetics in Hunan Province, Changsha 410000, Hunan Province, China
| | - Fang Zhu
- Institute of Reproduction and Stem Cell Engineering, School of Basic Medicine Science, Central South University, Changsha 410000, Hunan Province, China
| | - Zeng-Hui Huang
- Institute of Reproduction and Stem Cell Engineering, School of Basic Medicine Science, Central South University, Changsha 410000, Hunan Province, China
- Reproductive and Genetic Hospital of CITIC-Xiangya, Changsha 410000, Hunan Province, China
- Clinical Research Center for Reproduction and Genetics in Hunan Province, Changsha 410000, Hunan Province, China
| | - Huan Zhang
- Institute of Reproduction and Stem Cell Engineering, School of Basic Medicine Science, Central South University, Changsha 410000, Hunan Province, China
- Reproductive and Genetic Hospital of CITIC-Xiangya, Changsha 410000, Hunan Province, China
- Clinical Research Center for Reproduction and Genetics in Hunan Province, Changsha 410000, Hunan Province, China
| | - Li-Qing Fan
- Institute of Reproduction and Stem Cell Engineering, School of Basic Medicine Science, Central South University, Changsha 410000, Hunan Province, China
- Reproductive and Genetic Hospital of CITIC-Xiangya, Changsha 410000, Hunan Province, China
- Clinical Research Center for Reproduction and Genetics in Hunan Province, Changsha 410000, Hunan Province, China
| | - Jing-Yu Fan
- Institute of Reproduction and Stem Cell Engineering, School of Basic Medicine Science, Central South University, Changsha 410000, Hunan Province, China
- Department of Chemistry and Biochemistry, University of South Carolina, Columbia, SC 29208, United States
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22
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Prasitpuriprecha C, Jantama SS, Preeprem T, Pitakaso R, Srichok T, Khonjun S, Weerayuth N, Gonwirat S, Enkvetchakul P, Kaewta C, Nanthasamroeng N. Drug-Resistant Tuberculosis Treatment Recommendation, and Multi-Class Tuberculosis Detection and Classification Using Ensemble Deep Learning-Based System. Pharmaceuticals (Basel) 2022; 16:ph16010013. [PMID: 36678508 PMCID: PMC9864877 DOI: 10.3390/ph16010013] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 12/14/2022] [Accepted: 12/17/2022] [Indexed: 12/25/2022] Open
Abstract
This research develops the TB/non-TB detection and drug-resistant categorization diagnosis decision support system (TB-DRC-DSS). The model is capable of detecting both TB-negative and TB-positive samples, as well as classifying drug-resistant strains and also providing treatment recommendations. The model is developed using a deep learning ensemble model with the various CNN architectures. These architectures include EfficientNetB7, mobileNetV2, and Dense-Net121. The models are heterogeneously assembled to create an effective model for TB-DRC-DSS, utilizing effective image segmentation, augmentation, and decision fusion techniques to improve the classification efficacy of the current model. The web program serves as the platform for determining if a patient is positive or negative for tuberculosis and classifying several types of drug resistance. The constructed model is evaluated and compared to current methods described in the literature. The proposed model was assessed using two datasets of chest X-ray (CXR) images collected from the references. This collection of datasets includes the Portal dataset, the Montgomery County dataset, the Shenzhen dataset, and the Kaggle dataset. Seven thousand and eight images exist across all datasets. The dataset was divided into two subsets: the training dataset (80%) and the test dataset (20%). The computational result revealed that the classification accuracy of DS-TB against DR-TB has improved by an average of 43.3% compared to other methods. The categorization between DS-TB and MDR-TB, DS-TB and XDR-TB, and MDR-TB and XDR-TB was more accurate than with other methods by an average of 28.1%, 6.2%, and 9.4%, respectively. The accuracy of the embedded multiclass model in the web application is 92.6% when evaluated with the test dataset, but 92.8% when evaluated with a random subset selected from the aggregate dataset. In conclusion, 31 medical staff members have evaluated and utilized the online application, and the final user preference score for the web application is 9.52 out of a possible 10.
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Affiliation(s)
- Chutinun Prasitpuriprecha
- Department of Biopharmacy, Faculty of Pharmaceutical Sciences, Ubon Ratchathani University, Ubon Ratchathani 34190, Thailand
| | - Sirima Suvarnakuta Jantama
- Department of Biopharmacy, Faculty of Pharmaceutical Sciences, Ubon Ratchathani University, Ubon Ratchathani 34190, Thailand
| | - Thanawadee Preeprem
- Department of Biopharmacy, Faculty of Pharmaceutical Sciences, Ubon Ratchathani University, Ubon Ratchathani 34190, Thailand
| | - Rapeepan Pitakaso
- Department of Industrial Engineering, Faculty of Engineering, Ubon Ratchathani University, Ubon Ratchathani 34190, Thailand
| | - Thanatkij Srichok
- Department of Industrial Engineering, Faculty of Engineering, Ubon Ratchathani University, Ubon Ratchathani 34190, Thailand
| | - Surajet Khonjun
- Department of Industrial Engineering, Faculty of Engineering, Ubon Ratchathani University, Ubon Ratchathani 34190, Thailand
| | - Nantawatana Weerayuth
- Department of Mechanical Engineering, Faculty of Engineering, Ubon Ratchathani University, Ubon Ratchathani 34190, Thailand
| | - Sarayut Gonwirat
- Department of Computer Engineering and Automation, Faculty of Engineering and Industrial Technology, Kalasin University, Kalasin 46000, Thailand
| | - Prem Enkvetchakul
- Department of Information Technology, Faculty of Science, Buriram University, Buriram 31000, Thailand
| | - Chutchai Kaewta
- Department of Computer Science, Faculty of Computer Science, Ubon Ratchathani Rajabhat University, Ubon Ratchathani 34000, Thailand
| | - Natthapong Nanthasamroeng
- Department of Engineering Technology, Faculty of Industrial Technology, Ubon Ratchathani Rajabhat University, Ubon Ratchathani 34000, Thailand
- Correspondence: ; Tel.: +66-625474555
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23
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Prasitpuriprecha C, Pitakaso R, Gonwirat S, Enkvetchakul P, Preeprem T, Jantama SS, Kaewta C, Weerayuth N, Srichok T, Khonjun S, Nanthasamroeng N. Embedded AMIS-Deep Learning with Dialog-Based Object Query System for Multi-Class Tuberculosis Drug Response Classification. Diagnostics (Basel) 2022; 12:diagnostics12122980. [PMID: 36552987 PMCID: PMC9777254 DOI: 10.3390/diagnostics12122980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 11/23/2022] [Accepted: 11/25/2022] [Indexed: 11/29/2022] Open
Abstract
A person infected with drug-resistant tuberculosis (DR-TB) is the one who does not respond to typical TB treatment. DR-TB necessitates a longer treatment period and a more difficult treatment protocol. In addition, it can spread and infect individuals in the same manner as regular TB, despite the fact that early detection of DR-TB could reduce the cost and length of TB treatment. This study provided a fast and effective classification scheme for the four subtypes of TB: Drug-sensitive tuberculosis (DS-TB), drug-resistant tuberculosis (DR-TB), multidrug-resistant tuberculosis (MDR-TB), and extensively drug-resistant tuberculosis (XDR-TB). The drug response classification system (DRCS) has been developed as a classification tool for DR-TB subtypes. As a classification method, ensemble deep learning (EDL) with two types of image preprocessing methods, four convolutional neural network (CNN) architectures, and three decision fusion methods have been created. Later, the model developed by EDL will be included in the dialog-based object query system (DBOQS), in order to enable the use of DRCS as the classification tool for DR-TB in assisting medical professionals with diagnosing DR-TB. EDL yields an improvement of 1.17-43.43% over the existing methods for classifying DR-TB, while compared with classic deep learning, it generates 31.25% more accuracy. DRCS was able to increase accuracy to 95.8% and user trust to 95.1%, and after the trial period, 99.70% of users were interested in continuing the utilization of the system as a supportive diagnostic tool.
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Affiliation(s)
| | - Rapeepan Pitakaso
- Department of Industrial Engineering, Ubon Ratchathani University, Ubon Ratchathani 34190, Thailand
| | - Sarayut Gonwirat
- Department of Computer Engineering and Automation, Kalasin University, Kalasin 46000, Thailand
| | - Prem Enkvetchakul
- Department of Information Technology, Buriram Rajabhat University, Buriram 31000, Thailand
- Correspondence:
| | - Thanawadee Preeprem
- Faculty of Pharmaceutical Sciences, Ubon Ratchathani University, Ubon Ratchathani 34190, Thailand
| | | | - Chutchai Kaewta
- Department of Computer Science, Ubon Ratchathani Rajabhat University, Ubon Ratchathani 34000, Thailand
| | - Nantawatana Weerayuth
- Department of Mechanical Engineering, Ubon Ratchathani University, Ubon Ratchathani 34190, Thailand
| | - Thanatkij Srichok
- Department of Industrial Engineering, Ubon Ratchathani University, Ubon Ratchathani 34190, Thailand
| | - Surajet Khonjun
- Department of Industrial Engineering, Ubon Ratchathani University, Ubon Ratchathani 34190, Thailand
| | - Natthapong Nanthasamroeng
- Department of Engineering Technology, Ubon Ratchathani Rajabhat University, Ubon Ratchathani 34000, Thailand
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Huang ML, Liao YC. A lightweight CNN-based network on COVID-19 detection using X-ray and CT images. Comput Biol Med 2022; 146:105604. [PMID: 35576824 PMCID: PMC9090861 DOI: 10.1016/j.compbiomed.2022.105604] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 05/03/2022] [Accepted: 05/08/2022] [Indexed: 01/17/2023]
Abstract
BACKGROUND AND OBJECTIVES The traditional method of detecting COVID-19 disease mainly rely on the interpretation of computer tomography (CT) or X-ray images (X-ray) by doctors or professional researchers to identify whether it is COVID-19 disease, which is easy to cause identification mistakes. In this study, the technology of convolutional neural network is expected to be able to efficiently and accurately identify the COVID-19 disease. METHODS This study uses and fine-tunes seven convolutional neural networks including InceptionV3, ResNet50V2, Xception, DenseNet121, MobileNetV2, EfficientNet-B0, and EfficientNetV2 on COVID-19 detection. In addition, we proposes a lightweight convolutional neural network, LightEfficientNetV2, on small number of chest X-ray and CT images. Five-fold cross-validation was used to evaluate the performance of each model. To confirm the performance of the proposed model, LightEfficientNetV2 was carried out on three different datasets (NIH Chest X-rays, SARS-CoV-2 and COVID-CT). RESULTS On chest X-ray image dataset, the highest accuracy 96.50% was from InceptionV3 before fine-tuning; and the highest accuracy 97.73% was from EfficientNetV2 after fine-tuning. The accuracy of the LightEfficientNetV2 model proposed in this study is 98.33% on chest X-ray image. On CT images, the best transfer learning model before fine-tuning is MobileNetV2, with an accuracy of 94.46%; the best transfer learning model after fine-tuning is Xception, with an accuracy of 96.78%. The accuracy of the LightEfficientNetV2 model proposed in this study is 97.48% on CT image. CONCLUSIONS Compared with the SOTA, LightEfficientNetV2 proposed in this study demonstrates promising performance on chest X-ray images, CT images and three different datasets.
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